Citation and License

BMC Genomics 2012, 13:417
doi:10.1186/1471-2164-13-417

Published: 22 August 2012

Abstract

Background

Compared to classical genotyping, targeted next-generation sequencing (tNGS) can be custom-designed to interrogate entire genomic regions of interest, in
order to detect novel as well as known variants. To bring down the per-sample cost,
one approach is to pool barcoded NGS libraries before sample enrichment. Still, we
lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can
affect the quality of variant discovery. Therefore, we evaluated the impact of different
software tools and analytical approaches on the discovery of single nucleotide polymorphisms
(SNPs) in multiplexed tNGS data. To generate our own test model, we combined a sequence capture method with
NGS in three experimental stages of increasing complexity (E. coli genes, multiplexed E. coli, and multiplexed HapMap BRCA1/2 regions).

Results

We successfully enriched barcoded NGS libraries instead of genomic DNA, achieving
reproducible coverage profiles (Pearson correlation coefficients of up to 0.99) across
multiplexed samples, with <10% strand bias. However, the SNP calling quality was substantially
affected by the choice of tools and mapping strategy. With the aim of reducing computational
requirements, we compared conventional whole-genome mapping and SNP-calling with a
new faster approach: target-region mapping with subsequent ‘read-backmapping’ to the
whole genome to reduce the false detection rate. Consequently, we developed a combined
mapping pipeline, which includes standard tools (BWA, SAMtools, etc.), and tested
it on public HiSeq2000 exome data from the 1000 Genomes Project. Our pipeline saved
12 hours of run time per Hiseq2000 exome sample and detected ~5% more SNPs than the
conventional whole genome approach. This suggests that more potential novel SNPs may
be discovered using both approaches than with just the conventional approach.

Conclusions

We recommend applying our general ‘two-step’ mapping approach for more efficient SNP
discovery in tNGS. Our study has also shown the benefit of computing inter-sample SNP-concordances
and inspecting read alignments in order to attain more confident results.